function [ result ] = calcRankingLoss( Y, Y_out )
%Computing the hamming loss
%Outputs: the predicted outputs of the classifier, the output of the ith instance for the jth class is stored in Outputs(j,i)
%test_target: the actual labels of the test instances, if the ith instance belong to the jth class, test_target(j,i)=1, otherwise test_target(j,i)=-1

test_target = Y';
Outputs = Y_out';

[num_class,num_instance]=size(Outputs);
temp_Outputs=[];
temp_test_target=[];
for i=1:num_instance
    temp=test_target(:,i);
    if((sum(temp)~=num_class)&(sum(temp)~=-num_class))
        temp_Outputs=[temp_Outputs,Outputs(:,i)];
        temp_test_target=[temp_test_target,temp];
    end
end
Outputs=temp_Outputs;
test_target=temp_test_target;
[num_class,num_instance]=size(Outputs);

Label=cell(num_instance,1);
not_Label=cell(num_instance,1);
Label_size=zeros(1,num_instance);
for i=1:num_instance
    temp=test_target(:,i);
    Label_size(1,i)=sum(temp==ones(num_class,1));
    for j=1:num_class
        if(temp(j)==1)
            Label{i,1}=[Label{i,1},j];
        else
            not_Label{i,1}=[not_Label{i,1},j];
        end
    end
end

rankloss=0;
for i=1:num_instance
    temp=0;
    for m=1:Label_size(i)
        for n=1:(num_class-Label_size(i))
            if(Outputs(Label{i,1}(m),i)<=Outputs(not_Label{i,1}(n),i))
                temp=temp+1;
            end
        end
    end
    rl_binary(i)=temp/(m*n);
    rankloss=rankloss+temp/(m*n);
end

RankingLoss=rankloss/num_instance;
result = -RankingLoss;

end
